Data Subject Request API Version 1 and 2
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Getting Started
Identity
Alexa
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
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Step 5. Verify your connection
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Step 9. Test your local app
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 1. Create an input
Step 2. Create an output
Step 3. Verify output
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Overview
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Create an Input
Start capturing data
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Introduction
Introduction
Rudderstack
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Event
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Feed
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Audience
Feed
Event
Event
Event
Audience
Event
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Event
Event
Event
Audience
Feed
Event
Event
Event
Event
Event
Audience
Event
Event
Feed
Event
Event
Audience
Feed
Event
Event
Event
Custom Feed
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Event
Event
Audience
Audience
Audience
Event
Audience
Event
Event
Event
Event
Event
Event
Audience
Event
Audience
Event
Event
Audience
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Event
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Event
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Event
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Audience
Event
Audience
Event
Audience
Audience
Audience
Microsoft Ads Audience Integration
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Event
Event
Event
Event
Event
Event
Feed
Event
Event
Event
Event
Feed
Audience
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Event
Event
Event
Event
Event
Event
Feed
Event
Event
Feed
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Event
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Event
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Audience
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Feed
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Audience
Audience
Audience
Audience
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Feed
Audience
Event
Event
Audience
Audience
Event
Event
Event
Audience
Cookie Sync
Feed
Audience
Cookie Sync
Event
Audience
Event
Next Best Actions (NBAs) are a type of predictive attribute that determines which action or offer—from a set of choices that you define—is most likely to result in your desired business outcome for each user. By generating AI-powered recommendations at the level of individual users, NBAs take the guesswork out of your campaigns and help you achieve common business goals such as increasing retention, driving upsells, or improving engagement.
Unlike future behavior predictions, which estimate *who* is most likely to act, NBAs identify *what* to offer each user. Each NBA appears as a user attribute in mParticle and continuously updates as new data flows in, allowing your campaigns and recommendations to stay aligned with user behavior.
The following examples illustrate how next best actions can help you optimize decisions across common business goals.
Use a next best action to determine which membership tier offer is most likely to turn trial customers into paying members.
Focus on trial users or existing members on lower tiers who regularly attend the gym—for example, users who show moderate to high engagement with classes, personal training sessions, or equipment. Avoid targeting users who are already on the Elite membership plan.
Use NBA to assess different gym membership tiers—Basic, Premium, and Elite—as potential upgrades based on each user’s activity level and interests.
Let NBA analyze factors such as the user’s workout habits, class attendance, and upcoming promotions (for example, a new fitness class launch or training package) to recommend the best tier for each user and maximize the likelihood of an upsell.
Use a next best action to determine the best co-branded financial product offer for frequent shoppers.
Focus on customers who frequently shop in-store or online and demonstrate financial engagement, such as consistent use of loyalty rewards or payment methods. Avoid targeting customers who already have one of the available co-branded products.
Use NBA to evaluate financial products—Savings Account, Credit Card, and Line of Credit—as potential offers based on the user’s purchasing patterns, credit needs, and spending habits.
Let NBA analyze transaction history, average spending, and upcoming promotions (for example, a seasonal sale with cashback bonuses) to recommend the most suitable financial product for each user and increase the likelihood of new sign-ups.
Use a next best action to identify the best type of content to promote for increased user engagement and retention.
Focus on users who have shown moderate to high engagement but may need additional encouragement to stay active on the platform. Avoid targeting users who already consume content across multiple genres at high frequency.
Use NBA to evaluate different content types—Comedy, Horror, and Action—as potential recommendations based on each user’s viewing history and preferences.
Let NBA analyze factors such as past viewing patterns, session duration, and upcoming releases (for example, a new season in the user’s preferred genre) to recommend the most engaging content for each user, helping to boost activity and long-term retention.
Use a next best action to identify the best product category to recommend for increasing purchase value and order size.
Focus on customers who have made recent purchases but haven’t yet added multiple categories to their orders. Avoid targeting customers who consistently order complete meals that already include appetizers, mains, and desserts.
Use NBA to assess different product categories—Appetizer, Main, and Dessert—as potential recommendations based on the user’s ordering patterns and preferences.
Let NBA analyze factors such as past order history, favorite items, and upcoming promotions (for example, a limited-time combo deal or seasonal menu item) to recommend the most appealing product category for each user. This strategy encourages larger orders and higher purchase value.
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